arXiv AI
Sep 12

Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models

The paper introduces SOLID, a framework that enables operations research language models to self-improve without relying on verified answers or external evaluators. SOLID uses solver-generated artifacts from the model’s own rollouts to create pseudo-references, clustering objectives and applying group-relative advantages for dense self-supervision. Experiments on multiple OR benchmarks show that SOLID enhances solution accuracy for both general-purpose and OR-tuned models compared to outcome-only training.

By Rui Zhu, Minglong Cao, Chenyu Zhou, Jianghao Lin, Dongdong Ge
arXiv Computation and Language
Sep 1

S3Gym: Can LLMs Turn Self-Testing and Self-Judging into Self-Improvement?

S3Gym is an interactive benchmark designed to evaluate large language models (LLMs) on their ability to self-improve through self-testing, self-judging, and self-improvement. It separates permissive exploration from strict held-out evaluation across seven text-based games with executable environment verifiers. Experiments show that self-improvement varies by task, with different experience incorporation pathways (direct history, summary memory, or parameter training) yielding mixed results and highlighting the need for agents to transform feedback into executable, transferable policies.

By Jiajun Shi, Siyuan Tao, Yuhao Wu, Zexuan Wang, Jingyuan Zhang, Jiaheng Liu, Xinping Lei, Xinrong Zhang, Siyuan Fang, Zhewen Tan, Tianle Cai, Junhao Fang, Jiameng Huang, Yueyang Wang, Jinkai Liu, Yuxuan Zhang, Jian Yang, Zhoujun Li, Shen Yan, Wenhao Huang, Ge Zhang
arXiv AI
Aug 11

STEMMA: An Adversarial Multi-Agent Framework for Evaluating Self-Identity Consistency in LLMs

arXiv:2608. 08164v1 Announce Type: cross Abstract: Knowledge Distillation is a widely adopted technique in the training and fine-tuning of large language models (LLMs) enabling transfer of structured information and functional behavior from a large teacher model to a smaller student model while significantly reducing computational costs.

By Nuthakki Siva Gopala Krishna, Kanishka Jain